Multi-GPU Kubernetes Delivery Platform
Built a multi-GPU model build, hyperparameter tuning, and CI/CD pipeline on Kubernetes that doubled release velocity and lowered GPU spend per release.
- Release velocity
- 2×
- Build, tuning, and deploy
- Automated
- GPU cost per release
- Lower
Confidentiality note. Internal service names, proprietary scientific details, and employer-specific implementations are kept general here. Every scale figure, responsibility, technology, and outcome below comes from already-published facts in Sudhanva’s résumé and public profile.
Problem
Building, tuning, and packaging new model releases ran sequentially and required manual steps at each stage. Long runs tied up GPUs for hours and slowed down model deployments.
Scale and constraints
Scale
- Parallel multi-GPU model builds and hyperparameter tuning on Kubernetes.
- Automated build, tuning, verification, and deployment in GitHub Actions CI/CD.
Constraints
- Cut build and tuning wall-clock time while keeping model artifacts reproducible.
- Run regression and validation checks automatically before promoting a model to serving.
- Surface logs and test outputs clearly so engineers can debug failed builds without SSH access.
Sudhanva’s ownership
Sudhanva designed the multi-GPU Kubernetes build workflow and wired parallel tuning and deployment into CI/CD.
- Multi-GPU workflow design and Kubernetes GPU scheduling.
- Parallel hyperparameter tuning setup.
- GitHub Actions CI/CD pipelines for model verification and rollout.
Architecture at a safe level
Internal service names and proprietary scientific details are omitted below, while the data flow, orchestration, and operational setup reflect the production system.
- Split model build and hyperparameter tuning stages across parallel GPUs on the Kubernetes cluster.
- Moved build, validation, and deployment steps into GitHub Actions workflows.
- Released GPUs as soon as each stage finished, so faster builds also used fewer GPU hours.
- Tracked release cadence, deployment duration, and GPU hours per release.
Engineering decisions and tradeoffs
- Running tuning across parallel GPUs required stricter artifact versioning and job coordination than a single-GPU script.
- Archived build logs and validation outputs from every CI stage so failed runs could be diagnosed from the CI UI.
Results
- Doubled model release velocity by cutting deployment time in half.
- Replaced manual build, tuning, and deployment steps with an automated CI/CD pipeline.
- Lowered the GPU compute cost of each release by freeing accelerators the moment each parallel stage completed.
Reliability and operations
- CI/CD ran identical build, tuning, and verification checks on every model release.
- Automated promotion between training and serving removed manual copy-paste mistakes.
Transferable lessons
- Faster multi-GPU training only improves release cadence if packaging, evaluation, and deployment are automated too.
- Archiving logs and validation artifacts on every CI run let engineers debug failed model builds without SSHing into worker nodes.
Technologies
- Multi-GPU systems
- TensorFlow
- PyTorch
- CI/CD
- GitHub Actions
- Kubernetes
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